Three-dimensional reconstruction method, electronic device and storage medium

Abstract

The disclosure provides a 3-dimensional reconstruction method, an electronic device and a storage medium. The method includes: obtaining an image sequence pre-collected by a camera; obtaining nominal intrinsic parameters of the camera, and preset initial values of distortion coefficients of the camera, the camera being a camera for collecting the image sequence; calculating fluctuation ranges of the distortion coefficients; performing the 3D reconstruction on the image sequence based on the nominal intrinsic parameters, the initial values of the distortion coefficients, and the fluctuation ranges of the distortion coefficients; and during the 3D reconstruction, optimizing an obtained visual point cloud and a camera pose.

What is claimed is: 
     
       1. A 3-dimensional reconstruction method, comprising:
 obtaining an image sequence pre-collected by a camera; 
 obtaining nominal intrinsic parameters of the camera, and preset initial values of distortion coefficients of the camera, the camera being a camera for collecting the image sequence; 
 calculating fluctuation ranges of the distortion coefficients, comprising: extracting distorted line features of a part of images in the image sequence; and calculating the fluctuation ranges of the distortion coefficients according to the distorted line features; 
 performing the 3-dimensional reconstruction on the image sequence based on the nominal intrinsic parameters, the initial values of the distortion coefficients, and the fluctuation ranges of the distortion coefficients; and 
 during the 3-dimensional reconstruction, optimizing an obtained visual point cloud and a camera pose. 
 
     
     
       2. The method according to  claim 1 , further comprising:
 during the 3-dimensional reconstruction, optimizing a semantic element in the obtained visual point cloud, so that a point cloud of the semantic element meets geometric features corresponding to the semantic element. 
 
     
     
       3. The method according to  claim 1 , wherein the performing the 3-dimensional reconstruction on the image sequence comprises:
 selecting a first image and a second image from the image sequence; 
 performing the 3-dimensional reconstruction on the first image and the second image to obtain an initial visual point cloud; and 
 registering each of new images in the image sequence to obtain a point cloud of each of the new images; and 
 combining the point cloud of each of the new images into the initial visual point cloud. 
 
     
     
       4. The method according to  claim 3 , further comprising:
 during registering each of the new images, determining whether to optimize the distortion coefficients according to a change curve of distortion coefficient. 
 
     
     
       5. The method according to  claim 1 , wherein when the 3-dimensional reconstruction is performed on a target area of each image of the image sequence, the initial values of the distortion coefficients are 0, and the target area does not comprise an edge area of the image. 
     
     
       6. The method according to  claim 1 , wherein the nominal intrinsic parameters of the camera comprise a nominal focal length and a nominal optical center value of the camera. 
     
     
       7. The method according to  claim 1 , wherein the image sequence is an image sequence collected by crowdsourcing. 
     
     
       8. An electronic device, comprising:
 at least one processor; and 
 a memory coupled to the at least one processor; wherein, 
 the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor are caused to implement the following actions: 
 obtaining an image sequence pre-collected by a camera; 
 obtaining nominal intrinsic parameters of the camera, and preset initial values of distortion coefficients of the camera, the camera being a camera for collecting the image sequence; 
 calculating fluctuation ranges of the distortion coefficients, comprising: extracting distorted line features of a part of images in the image sequence; and calculating the fluctuation ranges of the distortion coefficients according to the distorted line features; 
 performing the 3-dimensional reconstruction on the image sequence based on the nominal intrinsic parameters, the initial values of the distortion coefficients, and the fluctuation ranges of the distortion coefficients; and 
 during the 3-dimensional reconstruction, optimizing an obtained visual point cloud and a camera pose. 
 
     
     
       9. The electronic device according to  claim 8 , wherein when the instructions are executed by the at least one processor, the at least one processor are caused to further implement the following actions:
 during the 3-dimensional reconstruction, optimizing a semantic element in the obtained visual point cloud, so that a point cloud of the semantic element meets geometric features corresponding to the semantic element. 
 
     
     
       10. The electronic device according to  claim 8 , wherein the performing the 3-dimensional reconstruction on the image sequence comprises:
 selecting a first image and a second image from the image sequence; 
 performing the 3-dimensional reconstruction on the first image and the second image to obtain an initial visual point cloud; and 
 registering each of new images in the image sequence to obtain a point cloud of each of the new images; and 
 combining the point cloud of each of the new images into the initial visual point cloud. 
 
     
     
       11. The electronic device according to  claim 10 , wherein when the instructions are executed by the at least one processor, the at least one processor are caused to further implement the following actions:
 during registering each of the new images, determining whether to optimize the distortion coefficients according to a change curve of distortion coefficient. 
 
     
     
       12. The electronic device according to  claim 8 , wherein when the 3-dimensional reconstruction is performed on a target area of each image of the image sequence, the initial values of the distortion coefficients are 0, and the target area does not comprise an edge area of the image. 
     
     
       13. The electronic device according to  claim 8 , wherein the nominal intrinsic parameters of the camera comprise a nominal focal length and a nominal optical center value of the camera. 
     
     
       14. The electronic device according to  claim 8 , wherein the image sequence is an image sequence collected by crowdsourcing. 
     
     
       15. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to make the computer implement a 3-dimensional reconstruction method, the method comprising:
 obtaining an image sequence pre-collected by a camera; 
 obtaining nominal intrinsic parameters of the camera, and preset initial values of distortion coefficients of the camera, the camera being a camera for collecting the image sequence; 
 calculating fluctuation ranges of the distortion coefficients, comprising: extracting distorted line features of a part of images in the image sequence; and calculating the fluctuation ranges of the distortion coefficients according to the distorted line features; 
 performing the 3-dimensional reconstruction on the image sequence based on the nominal intrinsic parameters, the initial values of the distortion coefficients, and the fluctuation ranges of the distortion coefficients; and 
 during the 3-dimensional reconstruction, optimizing an obtained visual point cloud and a camera pose. 
 
     
     
       16. The non-transitory computer-readable storage medium according to  claim 15 , wherein the method further comprises:
 during the 3-dimensional reconstruction, optimizing a semantic element in the obtained visual point cloud, so that a point cloud of the semantic element meets geometric features corresponding to the semantic element.